How to use user search log data to optimize synonyms and contextual relevance?

When it is necessary to optimize synonyms and contextual relevance, analyzing user search log data is a core method—by extracting high-frequency search terms, identifying variant expressions, and associated queries, we can accurately locate the vocabulary and scenarios actually used by users. High-frequency word extraction: Filter core words that repeatedly appear in the logs, such as "sneakers", "running shoes", and "athletic shoes". Incorporate these natural synonyms into the keyword library to avoid the limitation of relying solely on dictionary definitions. Variant identification: Capture spelling errors (e.g., "照相" [taking photos] vs. "照像" [taking photos]), colloquial expressions (e.g., "买电脑" [buy a computer] vs. "选购笔记本" [choose a laptop]), and supplement synonym variants to improve content coverage of diverse searches. Contextual scenario analysis: Count queries associated with search terms (e.g., "coffee" is often associated with "recommendation", "brand", and "recipe"), clarify the contextual collocations under user search intent, and optimize associated expressions in the content. It is recommended to regularly export 3-6 months of search logs, analyze word frequency and association strength with heat map tools, dynamically update the synonym library and contextual scenario tags, and make the content more in line with users' actual search habits.